The MItosis DOmain Generalization (MIDOG) challenge [1]} provides a means of testing different algorithms on cohorts of expertly annotated histology images for mitotic figure detection in the presence of a domain shift. To combat these challenges, we first normalise the stain intensities of all images provided to our model before passing images through our proposed hybrid mitosis detection pipeline. The hybrid analysis pipeline consists of (a) a mitotic candidate segmentation model and (b) refinement by a deep learning (DL) classifier. We generated ground truth (GT) segmentation masks of mitotic figures via a semi-automated DL model [2]}, [3]}.
The use of a pre-trained DL method for GT allows the DL models to exploit the important contextual information and treating this detection task as a segmentation task instead.
